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Development and Implementation of a Self-Optimizable Smart Lighting System Based on Learning Context in Classroom

Author

Listed:
  • Baoshi Sun

    (Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada)

  • Qiaoli Zhang

    (Suzhou Shuyan Information Technology Ltd., 18F, 58 Qing Long Gang Rd, Suzhou 215000, China)

  • Shi Cao

    (Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada)

Abstract

Illumination is one of the most important environmental factors in the classroom. Researchers have discovered that lighting settings have significant impact on students’ performance. Although light-emitting diode (LED) lighting systems can precisely control brightness level and correlated color temperature (CCT), existing designs of LED lighting control systems for classrooms are focused on energy-saving but lack context-based illumination control ability. In this study, a smart lighting system with continuous evolution capability was developed. It can adjust brightness, CCT, and illuminance distribution dynamically according to specific learning context. This system allows not only manual control, but also automatic switching of scenes by integrating with school schedules. Based on existing knowledge about lighting preference, 10 lighting modes confined in the comfortable zone of Kruithof curve were proposed for various classroom scenarios. Moreover, a classroom environmental data-processing framework for collecting and analyzing learning context, illumination settings, environmental data, and students’ performance data was introduced. This framework can help researchers explore the correlation between student performance and environmental parameters.

Suggested Citation

  • Baoshi Sun & Qiaoli Zhang & Shi Cao, 2020. "Development and Implementation of a Self-Optimizable Smart Lighting System Based on Learning Context in Classroom," IJERPH, MDPI, vol. 17(4), pages 1-26, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1217-:d:320391
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    References listed on IDEAS

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    1. Ana Castillo-Martinez & Jose-Amelio Medina-Merodio & Jose-Maria Gutierrez-Martinez & Juan Aguado-Delgado & Carmen De-Pablos-Heredero & Salvador Otón, 2018. "Evaluation and Improvement of Lighting Efficiency in Working Spaces," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    2. Frederico M. Bublitz & Arlene Oetomo & Kirti S. Sahu & Amethyst Kuang & Laura X. Fadrique & Pedro E. Velmovitsky & Raphael M. Nobrega & Plinio P. Morita, 2019. "Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things," IJERPH, MDPI, vol. 16(20), pages 1-24, October.
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